An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

Abstract

Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.

IoT-Enabled Flood Monitoring System for Enhanced Dam Surveillance and Risk Mitigation

“…Water reservoir or dam damages limits the following victims: water management, water resource allocation, flood alert and forecasting. An divine flood forecasting model was developed by Hayder et al, [2] based on exponential smoothing and long short term memory based deep learning structure supports all the above victims. One SMS gateway-based flood warning prototype was designed by Ramadhani et al, [3] through ultrasonic sensor for measuring water level together with an alarm buzzer on the Arduino core.…”

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

“…Applications for DL algorithms include the following: Predicting future flood occurrences-DL algorithms may be trained on significant historical flood data sets, such as rainfall data, river flow data, and satellite images. These algorithms contribute to the delivery of more precise and fast flood warnings, enabling greater preparedness and response [36]. Flood monitoring-near-real-time analysis of satellite images and other remote sensing data using DL algorithms can be used to monitor floods.…”

Improved Gender Detection and Age Estimation Using Multimodal Speech Datasets for speech Age Classification

“…To build a decision tree, the algorithm recursively partitions the dataset based on different features, aiming to separate classes or reduce variance within subsets. Metrics like Gini impurity, entropy, or information gain guide the splitting process (Hayder, Al-amiedy, et al, 2023) (Hayder, Al Ali, et al, 2023).During training, the algorithm selects the best feature and split point at each node based on the chosen criterion. This process continues until a stopping criterion is met, such as reaching a maximum depth or no further improvement.…”